1. Field of the Invention
The present invention relates to a method of diagnosing a processing system using principal components analysis (PCA), and more particularly to the utilization of an updated PCA.
2. Description of Related Art
Modeling and control of material processing systems, such as in semiconductor manufacturing, historically has been a very challenging task. Material processing systems typically run a variety of process recipes and products, each with unique chemical, mechanical, and electrical characteristics. Material processing systems also undergo frequent maintenance cycles wherein key parts are cleaned or replaced, and when periodic problems occur they are addressed with new hardware designs. In addition, there are particular process steps which have few substrate quality metrics directly related to their performance. Without integrated metrology, these measurements are delayed and often not measured for every substrate. These issues contribute to a complicated processing system that is already difficult to model with simple tools.
One approach to capture the behavior of a processing system in a model is to apply multivariate analysis, such as principal component analysis (PCA), to processing system data. However, due to process system drifts as well as changes in the trace data, a static PCA model is not sufficient to enable monitoring for a single processing system over a long horizon. Additionally, models developed for one processing system cannot carry over to another processing system, e.g., from one etch process chamber to another etch process chamber of the same design.